Implement LockFreeExponentiallyDecayingReservoir

metrics-benchmarks/src/main/java/com/codahale/metrics/benchmarks/ReservoirBenchmark.java

@@ -1,6 +1,8 @@
 package com.codahale.metrics.benchmarks;
 
 import com.codahale.metrics.ExponentiallyDecayingReservoir;
+import com.codahale.metrics.LockFreeExponentiallyDecayingReservoir;
+import com.codahale.metrics.Reservoir;
 import com.codahale.metrics.SlidingTimeWindowArrayReservoir;
 import com.codahale.metrics.SlidingTimeWindowReservoir;
 import com.codahale.metrics.SlidingWindowReservoir;
@@ -23,6 +25,7 @@ public class ReservoirBenchmark {
 
     private final UniformReservoir uniform = new UniformReservoir();
     private final ExponentiallyDecayingReservoir exponential = new ExponentiallyDecayingReservoir();
+    private final Reservoir lockFreeExponential = LockFreeExponentiallyDecayingReservoir.builder().build();
     private final SlidingWindowReservoir sliding = new SlidingWindowReservoir(1000);
     private final SlidingTimeWindowReservoir slidingTime = new SlidingTimeWindowReservoir(200, TimeUnit.MILLISECONDS);
     private final SlidingTimeWindowArrayReservoir arrTime = new SlidingTimeWindowArrayReservoir(200, TimeUnit.MILLISECONDS);
@@ -60,6 +63,12 @@ public Object perfSlidingTimeWindowReservoir() {
         return slidingTime;
     }
 
+    @Benchmark
+    public Object perfLockFreeExponentiallyDecayingReservoir() {
+        lockFreeExponential.update(nextValue);
+        return lockFreeExponential;
+    }
+
     public static void main(String[] args) throws RunnerException {
         Options opt = new OptionsBuilder()
             .include(".*" + ReservoirBenchmark.class.getSimpleName() + ".*")

metrics-core/src/main/java/com/codahale/metrics/LockFreeExponentiallyDecayingReservoir.java

@@ -0,0 +1,270 @@
+package com.codahale.metrics;
+
+import com.codahale.metrics.WeightedSnapshot.WeightedSample;
+
+import java.time.Duration;
+import java.util.Objects;
+import java.util.concurrent.ConcurrentSkipListMap;
+import java.util.concurrent.ThreadLocalRandom;
+import java.util.concurrent.atomic.AtomicIntegerFieldUpdater;
+import java.util.concurrent.atomic.AtomicReferenceFieldUpdater;
+import java.util.function.BiConsumer;
+
+/**
+ * A lock-free exponentially-decaying random reservoir of {@code long}s. Uses Cormode et al's
+ * forward-decaying priority reservoir sampling method to produce a statistically representative
+ * sampling reservoir, exponentially biased towards newer entries.
+ *
+ * @see <a href="http://dimacs.rutgers.edu/~graham/pubs/papers/fwddecay.pdf">
+ * Cormode et al. Forward Decay: A Practical Time Decay Model for Streaming Systems. ICDE '09:
+ * Proceedings of the 2009 IEEE International Conference on Data Engineering (2009)</a>
+ *
+ * {@link LockFreeExponentiallyDecayingReservoir} is based closely on the {@link ExponentiallyDecayingReservoir},
+ * however it provides looser guarantees while completely avoiding locks.
+ *
+ * Looser guarantees:
+ * <ul>
+ *     <li> Updates which occur concurrently with rescaling may be discarded if the orphaned state node is updated after
+ *     rescale has replaced it. This condition has a greater probability as the rescale interval is reduced due to the
+ *     increased frequency of rescaling. {@link #rescaleThresholdNanos} values below 30 seconds are not recommended.
+ *     <li> Given a small rescale threshold, updates may attempt to rescale into a new bucket, but lose the CAS race
+ *     and update into a newer bucket than expected. In these cases the measurement weight is reduced accordingly.
+ *     <li>In the worst case, all concurrent threads updating the reservoir may attempt to rescale rather than
+ *     a single thread holding an exclusive write lock. It's expected that the configuration is set such that
+ *     rescaling is substantially less common than updating at peak load. Even so, when size is reasonably small
+ *     it can be more efficient to rescale than to park and context switch.
+ * </ul>
+ *
+ * @author <a href="mailto:ckozak@ckozak.net">Carter Kozak</a>
+ */
+public final class LockFreeExponentiallyDecayingReservoir implements Reservoir {
+
+    private static final double SECONDS_PER_NANO = .000_000_001D;
+    private static final AtomicReferenceFieldUpdater<LockFreeExponentiallyDecayingReservoir, State> stateUpdater =
+            AtomicReferenceFieldUpdater.newUpdater(LockFreeExponentiallyDecayingReservoir.class, State.class, "state");
+
+    private final int size;
+    private final long rescaleThresholdNanos;
+    private final Clock clock;
+
+    private volatile State state;
+
+    private static final class State {
+
+        private static final AtomicIntegerFieldUpdater<State> countUpdater =
+                AtomicIntegerFieldUpdater.newUpdater(State.class, "count");
+
+        private final double alphaNanos;
+        private final int size;
+        private final long startTick;
+        // Count is updated after samples are successfully added to the map.
+        private final ConcurrentSkipListMap<Double, WeightedSample> values;
+
+        private volatile int count;
+
+        State(
+                double alphaNanos,
+                int size,
+                long startTick,
+                int count,
+                ConcurrentSkipListMap<Double, WeightedSample> values) {
+            this.alphaNanos = alphaNanos;
+            this.size = size;
+            this.startTick = startTick;
+            this.values = values;
+            this.count = count;
+        }
+
+        private void update(long value, long timestampNanos) {
+            double itemWeight = weight(timestampNanos - startTick);
+            double priority = itemWeight / ThreadLocalRandom.current().nextDouble();
+            boolean mapIsFull = count >= size;
+            if (!mapIsFull || values.firstKey() < priority) {
+                addSample(priority, value, itemWeight, mapIsFull);
+            }
+        }
+
+        private void addSample(double priority, long value, double itemWeight, boolean bypassIncrement) {
+            if (values.putIfAbsent(priority, new WeightedSample(value, itemWeight)) == null
+                    && (bypassIncrement || countUpdater.incrementAndGet(this) > size)) {
+                values.pollFirstEntry();
+            }
+        }
+
+        /* "A common feature of the above techniques—indeed, the key technique that
+         * allows us to track the decayed weights efficiently—is that they maintain
+         * counts and other quantities based on g(ti − L), and only scale by g(t − L)
+         * at query time. But while g(ti −L)/g(t−L) is guaranteed to lie between zero
+         * and one, the intermediate values of g(ti − L) could become very large. For
+         * polynomial functions, these values should not grow too large, and should be
+         * effectively represented in practice by floating point values without loss of
+         * precision. For exponential functions, these values could grow quite large as
+         * new values of (ti − L) become large, and potentially exceed the capacity of
+         * common floating point types. However, since the values stored by the
+         * algorithms are linear combinations of g values (scaled sums), they can be
+         * rescaled relative to a new landmark. That is, by the analysis of exponential
+         * decay in Section III-A, the choice of L does not affect the final result. We
+         * can therefore multiply each value based on L by a factor of exp(−α(L′ − L)),
+         * and obtain the correct value as if we had instead computed relative to a new
+         * landmark L′ (and then use this new L′ at query time). This can be done with
+         * a linear pass over whatever data structure is being used."
+         */
+        State rescale(long newTick) {
+            long durationNanos = newTick - startTick;
+            double scalingFactor = Math.exp(-alphaNanos * durationNanos);
+            int newCount = 0;
+            ConcurrentSkipListMap<Double, WeightedSample> newValues = new ConcurrentSkipListMap<>();
+            if (Double.compare(scalingFactor, 0) != 0) {
+                RescalingConsumer consumer = new RescalingConsumer(scalingFactor, newValues);
+                values.forEach(consumer);
+                // make sure the counter is in sync with the number of stored samples.
+                newCount = consumer.count;
+            }
+            // It's possible that more values were added while the map was scanned, those with the
+            // minimum priorities are removed.
+            while (newCount > size) {
+                Objects.requireNonNull(newValues.pollFirstEntry(), "Expected an entry");
+                newCount--;
+            }
+            return new State(alphaNanos, size, newTick, newCount, newValues);
+        }
+
+        private double weight(long durationNanos) {
+            return Math.exp(alphaNanos * durationNanos);
+        }
+    }
+
+    private static final class RescalingConsumer implements BiConsumer<Double, WeightedSample> {
+        private final double scalingFactor;
+        private final ConcurrentSkipListMap<Double, WeightedSample> values;
+        private int count;
+
+        RescalingConsumer(double scalingFactor, ConcurrentSkipListMap<Double, WeightedSample> values) {
+            this.scalingFactor = scalingFactor;
+            this.values = values;
+        }
+
+        @Override
+        public void accept(Double priority, WeightedSample sample) {
+            double newWeight = sample.weight * scalingFactor;
+            if (Double.compare(newWeight, 0) == 0) {
+                return;
+            }
+            WeightedSample newSample = new WeightedSample(sample.value, newWeight);
+            if (values.put(priority * scalingFactor, newSample) == null) {
+                count++;
+            }
+        }
+    }
+
+    private LockFreeExponentiallyDecayingReservoir(int size, double alpha, Duration rescaleThreshold, Clock clock) {
+        // Scale alpha to nanoseconds
+        double alphaNanos = alpha * SECONDS_PER_NANO;
+        this.size = size;
+        this.clock = clock;
+        this.rescaleThresholdNanos = rescaleThreshold.toNanos();
+        this.state = new State(alphaNanos, size, clock.getTick(), 0, new ConcurrentSkipListMap<>());
+    }
+
+    @Override
+    public int size() {
+        return Math.min(size, state.count);
+    }
+
+    @Override
+    public void update(long value) {
+        long now = clock.getTick();
+        rescaleIfNeeded(now).update(value, now);
+    }
+
+    private State rescaleIfNeeded(long currentTick) {
+        // This method is optimized for size so the check may be quickly inlined.
+        // Rescaling occurs substantially less frequently than the check itself.
+        State stateSnapshot = this.state;
+        if (currentTick - stateSnapshot.startTick >= rescaleThresholdNanos) {
+            return doRescale(currentTick, stateSnapshot);
+        }
+        return stateSnapshot;
+    }
+
+    private State doRescale(long currentTick, State stateSnapshot) {
+        State newState = stateSnapshot.rescale(currentTick);
+        if (stateUpdater.compareAndSet(this, stateSnapshot, newState)) {
+            // newState successfully installed
+            return newState;
+        }
+        // Otherwise another thread has won the race and we can return the result of a volatile read.
+        // It's possible this has taken so long that another update is required, however that's unlikely
+        // and no worse than the standard race between a rescale and update.
+        return this.state;
+    }
+
+    @Override
+    public Snapshot getSnapshot() {
+        State stateSnapshot = rescaleIfNeeded(clock.getTick());
+        return new WeightedSnapshot(stateSnapshot.values.values());
+    }
+
+    public static Builder builder() {
+        return new Builder();
+    }
+
+    /**
+     * By default this uses a size of 1028 elements, which offers a 99.9%
+     * confidence level with a 5% margin of error assuming a normal distribution, and an alpha
+     * factor of 0.015, which heavily biases the reservoir to the past 5 minutes of measurements.
+     */
+    public static final class Builder {
+        private static final int DEFAULT_SIZE = 1028;
+        private static final double DEFAULT_ALPHA = 0.015D;
+        private static final Duration DEFAULT_RESCALE_THRESHOLD = Duration.ofHours(1);
+
+        private int size = DEFAULT_SIZE;
+        private double alpha = DEFAULT_ALPHA;
+        private Duration rescaleThreshold = DEFAULT_RESCALE_THRESHOLD;
+        private Clock clock = Clock.defaultClock();
+
+        private Builder() {}
+
+        /**
+         * Maximum number of samples to keep in the reservoir. Once this number is reached older samples are
+         * replaced (based on weight, with some amount of random jitter).
+         */
+        public Builder size(int value) {
+            if (value <= 0) {
+                throw new IllegalArgumentException(
+                        "LockFreeExponentiallyDecayingReservoir size must be positive: " + value);
+            }
+            this.size = value;
+            return this;
+        }
+
+        /**
+         * Alpha is the exponential decay factor. Higher values bias results more heavily toward newer values.
+         */
+        public Builder alpha(double value) {
+            this.alpha = value;
+            return this;
+        }
+
+        /**
+         * Interval at which this reservoir is rescaled.
+         */
+        public Builder rescaleThreshold(Duration value) {
+            this.rescaleThreshold = Objects.requireNonNull(value, "rescaleThreshold is required");
+            return this;
+        }
+
+        /**
+         * Clock instance used for decay.
+         */
+        public Builder clock(Clock value) {
+            this.clock = Objects.requireNonNull(value, "clock is required");
+            return this;
+        }
+
+        public Reservoir build() {
+            return new LockFreeExponentiallyDecayingReservoir(size, alpha, rescaleThreshold, clock);
+        }
+    }
+}